Compiled by Citation Safe (Digital Empire Holdings LLC) from the court’s published sanction order (decided September 15, 2026). Quotes and case details come from the order itself — check the primary source before relying on them.

Sanction teardown · HRT Ontario, Canada · 2026-09-15

B.V. v. Ramanah

What happened

In HRT Ontario, Canada, a filing relied on an unnamed/unconfirmed AI tool to help draft legal argument. The court identified the following problems with the citations in that filing:

Fabricated (Case Law)

The respondents cited four cases that did not exist. The Tribunal found that the supplied citation numbers belonged to different Tribunal decisions involving different named parties and therefore rejected the authorities as hallucinated.
Misrepresented (Case Law)

The respondents cited Harriott v. National Money Mart, 2010 HRTO 353, but the Tribunal found that the case did not stand for the proposition asserted in the written submissions.
Fabricated (Exhibits & Submissions)

The Tribunal found that the respondents’ written submissions inaccurately described the evidence, including by asserting that B.V.’s testimony contained conflicting accounts about the frequency of events when she had worked only one day.

Which AI tool

an unnamed/unconfirmed AI tool. Note: Charlotin's public database records tool attribution only where a court order, brief, or reporting on the matter states it explicitly; "unidentified" or "implied" means the record indicates AI use but does not name a specific product — we do not guess.

Outcome

Written Submissions Ignored

How Citation Safe would have caught this

Citation Safe runs three deterministic layers before a brief is filed: (1) does the citation exist against CourtListener's database of published opinions, (2) if quoted, does that exact language appear in the source, (3) does the cited case actually support the proposition it is cited for. Fabricated case citations fail Layer 1. Fabricated or misattributed quotations fail Layer 2 even when the underlying case is real. Misrepresented holdings — a real case cited for a proposition it does not support — are the target of Layer 3. None of these checks involve asking another language model whether the citation looks right; they are lookups and text-matches against the actual source, which is why a hallucinated citation has to survive a direct lookup against the authoritative source — not another model's opinion — to earn a VERIFIED stamp; our measured false-verify rate is published live at /quality.

Check a brief before you file it → · See our live false-verify rate

Source: https://www.damiencharlotin.com/documents/3217/B.V._v._Ramanah_Canada_15-09-2026.pdf, via Damien Charlotin's public AI Hallucination Cases Database (CC0).